Please use this identifier to cite or link to this item: https://doi.org/10.5705/ss.2010.216
Title: Extended BIC for small-n-large-P sparse GLM
Authors: Chen, J.
Chen, Z. 
Keywords: Consistency
Exponential family
Extended Bayes information criterion
Feature selection
Generalized linear model
Small-n-large-P
Issue Date: Apr-2012
Citation: Chen, J., Chen, Z. (2012-04). Extended BIC for small-n-large-P sparse GLM. Statistica Sinica 22 (2) : 555-574. ScholarBank@NUS Repository. https://doi.org/10.5705/ss.2010.216
Abstract: The small-n-large-P situation has become common in genetics research, medical studies, risk management, and other fields. Feature selection is crucial in these studies yet poses a serious challenge. The traditional criteria such as AIC, BIC, and cross-validation choose too many features. In this paper, we examine the variable selection problem under the generalized linear models. We study the approach where a prior takes specific account of the small-n-large-P situation. The criterion is shown to be variable selection consistent under generalized linear models. We also report simulation results and a data analysis to illustrate the effectiveness of EBIC for feature selection.
Source Title: Statistica Sinica
URI: http://scholarbank.nus.edu.sg/handle/10635/105148
ISSN: 10170405
DOI: 10.5705/ss.2010.216
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